1

In the addon of the session, we counted the charging stations located within North-Rhine Westphalia (NRW). Still, we did not show how to get a point layer of NRW charging stations (“charger_nrw”).

Subset the data file yourself by relying on the spatial information of the file charging_points_ger.csv and a polygon of NRW. There are two ways to achieve this. How many chargers are located within NRW?

You need two datasets for that: the point layer charging_points_ger.csv (remember to adjust the crs) in the ./data folder and polygons of NRW. For the latter, you can again use the osmdata syntax.

There are two functions you can explore: sf::st_join and sf::st_intersection(). The default of sf::st_join() will leave you with a ‘left-join’ and returns a data object with all chargers and matching district information for those which are located within NRW. You can reset the option to perform an ‘inner-join’ and keep only the observation which lay within the predefined area (sf::st_join(x , y, join = "", left = FALSE)).

2

Did the operationalization of train station accessbility convince you? The INKAR data base offers another approach: Proportion of residents with max. 1000m linear distance to the nearest public transport stop in the district. We do have everything to create this indicator. You can run this code to load all the data you need. What is the mean share of residents with max. 1000m linear distance to the nearest train station in a 5km neighbourhood of our fake respondents?

nrw <-
  osmdata::getbb(
    "Nordrhein-Westfalen", 
    format_out = "sf_polygon"
  ) %>% 
  .$multipolygon %>% 
  sf::st_transform(3035) 

set.seed(1234)

fake_coordinates <-
  sf::st_sample(nrw, 1000) %>% 
  sf::st_sf() %>% 
  dplyr::mutate(
    id_2 = 
      stringi::stri_rand_strings(10000, 10) %>% 
      sample(1000, replace = FALSE)
  )

nrw_pt_trainstops <- sf::st_read("./data/nrw_pt_osmtrainstops.shp", crs = 3035)

inhabitants_ger <-
  z11::z11_get_100m_attribute(Einwohner)

As always, there are several ways to do this. Anne tried to keep the workflow as close to the function taught in this course and suggests following steps: 1. Create a point layer with the centroids of all grids in NRW based on the z11 population layer. 2. Calculate the distance to the next train station for each grid. 3. Create a column that equals 0 if the distance to the next train station is >1000m and contains the number of inhabitants if <1000. 4. Rasterize the sf data object to receive two raster object: number of inhabitants and number of inhabitants with max 1000m distance to next train station. 5. Calculate the mean for the 5km buffer for each “respondent” for each raster. 6. Calculate the share.

In the add-on slides of the raster session Stefan introduced gave some information on how to transform raster to points (and back). To get a sf point layer of the raster object you can use terra::as.points() %>% sf::st_as_sf(). To rasterize the object you can use terra::rast(vals = .$colname, resolution = 100).